The HAPPE plus Event-Related (HAPPE+ER) software: A standardized preprocessing pipeline for event-related potential analyses
Bibliographic record
Abstract
Event-Related Potential (ERP) designs are a common method for interrogating neurocognitive function with electroencephalography (EEG). However, the traditional method of preprocessing ERP data is manual-editing - a subjective, time-consuming processes. A number of automated pipelines have recently been created to address the need for standardization, automation, and quantification of EEG data pre-processing; however, few are optimized for ERP analyses (especially in developmental or clinical populations). We propose and validate the HAPPE plus Event-Related (HAPPE+ER) software, a standardized and automated pre-processing pipeline optimized for ERP analyses across the lifespan. HAPPE+ER processes event-related potential data from raw files through preprocessing and generation of event-related potentials for statistical analyses. HAPPE+ER also includes post-processing reports of both data quality and pipeline quality metrics to facilitate the evaluation and reporting of data processing in a standardized manner. Finally, HAPPE+ER includes post-processing scripts to facilitate validating HAPPE+ER performance and/or comparing to performance of other preprocessing pipelines in users' own data via simulated ERPs. We describe multiple approaches with simulated and real ERP data to optimize pipeline performance and compare to other methods and pipelines. HAPPE+ER software is freely available under the terms of GNU General Public License at https://www.gnu.org/licenses/#GPL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".